Genetic Prediction Server Using Machine Learning Models
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Solution Overview
Problem
Associating specific phenotypic traits with genetic variants is complex due to the vastness of the human genome, presence of non-coding regions, and many-to-many associations, making it difficult to identify genetic contributions to traits without extensive individual study.
Innovation Solution
Machine learning models, such as neural networks, are used to predictively assign characteristics or genetic variants based on genomic data and individual records, including genetic variants, EHRs, and behavioral data, allowing for updates in predictive logic based on accuracy scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to associate genetic variants with phenotypic traits, then measurement precision may be maintained through close individual study, but device complexity and loss of time increase significantly due to the vastness of the genome and many-to-many associations
Solution Approach 1:
The patent introduces machine learning models as intermediary systems that process genomic data and predict phenotypic traits. These models serve as mediators between the complex genetic data and the desired trait predictions, handling the computational complexity while providing accurate associations. The system acts as an intermediary layer that translates vast genomic information into actionable phenotypic predictions without requiring direct manual analysis of each genetic variant.
Solution Approach 2:
The patent replaces traditional mechanical/manual methods of genetic analysis with automated machine learning systems. Instead of relying on close individual study and manual correlation of genetic variants with traits, the system uses computational algorithms to automatically process genomic data and predict phenotypic outcomes, significantly reducing the complexity and time required for analysis.
2Measurement precision
If close individual study is conducted to identify traits and genetic variants, then measurement precision improves, but loss of time increases due to the extensive data collection and analysis required
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models on large datasets of genomic information and known trait associations. This pre-processing allows the models to quickly make accurate predictions without requiring extensive individual study for each new case. The system performs preliminary data collection and model training in advance, enabling rapid analysis when actual trait prediction is needed.
Solution Approach 2:
The patent replaces time-consuming manual analysis and close individual study with automated machine learning systems that can process genomic data rapidly. The computational system substitutes for the slow, methodical human analysis process, maintaining high precision while dramatically reducing the time required for trait identification and genetic variant association.
3Measurement precision
If comprehensive data collection is performed to identify hard-to-quantify traits, then measurement precision improves, but device complexity and loss of substance increase due to the vast amounts of data required
Solution Approach 1:
The patent applies the extraction principle by using machine learning models to identify and extract only the most relevant genetic variants and data features that contribute to trait prediction. Instead of requiring comprehensive analysis of all genomic data, the system extracts the critical subset of information needed for accurate predictions, reducing the overall data volume required while maintaining measurement precision for complex traits.
4Productivity
If machine learning models are used to predictively assign characteristics, then productivity increases through automated analysis, but measurement precision may worsen without accurate scoring functions and feedback mechanisms
Solution Approach 1:
The patent implements feedback mechanisms where the accuracy of predictive assignments is continuously evaluated and used to improve the machine learning models. The system incorporates scoring functions that assess prediction quality and uses this feedback to refine models, ensuring that productivity gains from automation do not compromise measurement precision. The feedback loop allows the system to learn from errors and improve predictive accuracy over time.
Data Source
AI summary
Systems and methods are provided for performing predictive assignments pertaining to genetic information. One embodiment is a system that includes a genetic prediction server. The genetic prediction server includes an interface that acquires records that each indicate one or more genetic variants determined to exist within an individual, and a controller. The controller selects one or more machine learning models that utilize the genetic variants as input, and loads the machine learning models. For each individual in the records: the controller predictively assigns at least one characteristic to that individual by operating the machine learning models based on at least one genetic variant indicated in the records for that individual. The controller also generates a report indicating at least one predictively assigned characteristic for at least one individual, and transmits a command via the interface for presenting the report at a display.